Intuitive Understanding of Kalman Filtering with MATLAB®

Intuitive Understanding of Kalman Filtering with MATLAB®
  • eBook:
    Intuitive Understanding of Kalman Filtering with MATLAB®
  • Author:
    Armando Barreto, Malek Adjouadi, Francisco R. Ortega, Nonnarit O-larnnithipong
  • Edition:
    1 edition
  • Categories:
  • Data:
    September 7, 2020
  • ISBN:
  • ISBN-13:
  • Language:
  • Pages:
    248 pages
  • Format:

Book Description
The emergence of affordable micro sensors, such as MEMS Inertial Measurement Systems, are applied in embedded systems and Internet-of-Things devices. This has brought techniques such as Kalman Filtering, which are capable of combining information from multiple sensors or sources, to the interest of students and hobbyists. This book will explore the necessary background concepts, helping a much wider audience of readers develop an understanding and intuition that will enable them to follow the explanation for the Kalman Filtering algorithm.

Key Features:
  • Provides intuitive understanding of Kalman Filtering approach
  • Succinct overview of concepts to enhance accessibility and appeal to a wide audience
  • Interactive learning techniques with code examples


PART I Background
CHAPTER 1. System Models and Random Variables
CHAPTER 2. Multiple Random Sequences Considered Jointly
CHAPTER 3. Conditional Probability, Bayes’ Rule and Bayesian Estimation

PART II Where Does Kalman Filtering Apply and What Does It Intend to Do?
CHAPTER 4. A Simple Scenario Where Kalman Filtering May Be Applied
CHAPTER 5. General Scenario Addressed by Kalman Filtering and Specifc Cases
CHAPTER 6. Arriving at the Kalman Filter Algorithm
CHAPTER 7. Refecting on the Meaning and Evolution of the Entities in the Kalman Filter Algorithm

PART III Examples in MATLAB®
CHAPTER 8. MATLAB® Function to Implement and Exemplify the Kalman Filter
CHAPTER 9. Univariate Example of Kalman Filter in MATLAB®
CHAPTER 10. Multivariate Example of Kalman Filter in MATLAB®

PART IV Kalman Filtering Application to IMUs
CHAPTER 11. Kalman Filtering Applied to 2-Axis Attitude Estimation from Real IMU Signals
CHAPTER 12. Real-Time Kalman Filtering Application to Attitude Estimation from IMU Signals

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